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Gene expression signatures (GES) connect phenotypes to differential messenger RNA (mRNA) expression of genes, providing a powerful approach to define cellular identity, function, and the effects of perturbations. The use of GES has suffered from vague assessment criteria and limited reproducibility. Because the structure of proteins defines the functional capability of genes, we hypothesized that enrichment of structural features could be a generalizable representation of gene sets. We derive structural gene expression signatures (sGES) using features from multiple levels of protein structure (e.g., domain and fold) encoded by the mRNAs in GES. Comprehensive analyses of data from the Genotype-Tissue Expression Project (GTEx), the all RNA-seq and ChIP-seq sample and signature search (ARCHS4) database, and mRNA expression of drug effects on cardiomyocytes show that sGES are useful for characterizing biological phenomena. sGES enable phenotypic characterization across experimental platforms, facilitates interoperability of expression datasets, and describe drug action on cells., Competing Interests: Competing interest statement: R.R. and A.S. are co-founders of Aichemy Inc.

We show theoretically and experimentally a mechanism behind the emergence of wide or bimodal protein distributions in biochemical networks with nonlinear input-output characteristics (the dose-response curve) and variability in protein abundance. Large cell-to-cell variation in the nonlinear dose-response characteristics can be beneficial to facilitate two distinct groups of response levels as opposed to a graded response. Under the circumstances that we quantify mathematically, the two distinct responses can coexist within a cellular population, leading to the emergence of a bimodal protein distribution. Using flow cytometry, we demonstrate the appearance of wide distributions in the hypoxia-inducible factor-mediated response network in HCT116 cells. With help of our theoretical framework, we perform a novel calculation of the magnitude of cell-to-cell heterogeneity in the dose-response obtained experimentally., (© 2014 The Author(s) Published by the Royal Society. All rights reserved.)

Cooperativity of ligand-receptor binding influences the input-output behavior of a biochemical system and thus is an important determinant of its physiological function. Canonically, such cooperativity is understood in terms of ligand-receptor binding affinity, where an initial binding event changes the affinity for subsequent binding events. Here, we demonstrate that dimerization-a simple yet pervasive signaling motif across biology-can have significant control over cooperativity and even dominate over the canonical mechanism. Through an exhaustive parameter sensitivity analysis of a general kinetic model for signal-mediated dimerization, we show that quantitative modulation of dimerization processes can reinforce, eliminate, and even reverse cooperativity imposed by the canonical allosteric ligand-receptor binding affinity mechanism. The favored accumulation of stoichiometrically asymmetric dimers (those with ligand-receptor stoichiometry of 1 : 2) is a major determinant of dimerization-based cooperativity control. However, simulations demonstrate that favoring accumulation of such stoichiometrically asymmetric dimers can either increase or decrease cooperativity, and thus the quantitative relationship between stoichiometrically asymmetric dimers and cooperativity is highly dependent on the parameter values of the particular system of interest. These results suggest that the dimerization motif provides a novel mechanism for both generating and quantitatively tuning cooperativity that, due to the ubiquity of dimerization motifs in biochemical systems, may play a major role in a host of biological functions. Thus, the canonical, allosteric view of cooperativity is incomplete without considering dimerization effects, which is of particular importance as dimerization is often a necessary feature of the allosteric mechanism.

The abundance of a particular protein varies both over time within a single mammalian cell and between cells of a genetically identical population. Here, we investigate the properties of such noisy protein expression in mammalian cells by combining theoretical and experimental approaches. The gamma distribution model is well-known to describe cell-to-cell variability in protein expression in a variety of common scenarios. This model predicts, and experiments show, that when protein levels are manipulated by altering transcription rates or mRNA half-life, protein expression noise, defined as the squared coefficient of variation, is constant. In contrast, we also demonstrate that when protein levels are manipulated by changing protein half-life, as mean levels increase, noise decreases. Thus, in mammalian cells, the scaling relationship between mean protein levels and expression noise depends on how mean levels are perturbed. Therefore it may be important to consider how common experimental manipulations of protein expression affect not only mean levels, but also noise levels. In the context of knockdown experiments, natural cell-to-cell variability in protein expression implies that a particular cell from the knockdown population may have higher protein levels than a cell from the control population. Simulations and experimental data suggest that approximately three-fold knockdown in mean expression levels can reduce such so-called "overlap probability" to less than ~10%. This has implications for the interpretation of knockdown experiments when the readout is a single cell measure.